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Remove position analysis

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This commit is contained in:
Elias Projahn 2021-11-22 15:16:05 +01:00
parent 255123c74f
commit 599f09a52f
5 changed files with 15 additions and 52 deletions
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6
R/analyze.R
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@ -32,13 +32,7 @@ analyze <- function(preset, progress = NULL) {
# - `score` Score for the gene between 0.0 and 1.0. # - `score` Score for the gene between 0.0 and 1.0.
methods <- list( methods <- list(
"clusteriness" = clusteriness, "clusteriness" = clusteriness,
"clusteriness_positions" = function(...) {
clusteriness(..., use_positions = TRUE)
},
"correlation" = correlation, "correlation" = correlation,
"correlation_positions" = function(...) {
correlation(..., use_positions = TRUE)
},
"neural" = neural, "neural" = neural,
"proximity" = proximity "proximity" = proximity
) )

11
R/clusteriness.R
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@ -36,11 +36,11 @@ clusteriness_priv <- function(data, height = 1000000) {
} }
# Process genes clustering their distance to telomeres. # Process genes clustering their distance to telomeres.
clusteriness <- function(preset, use_positions = FALSE, progress = NULL) { clusteriness <- function(preset, progress = NULL) {
species_ids <- preset$species_ids species_ids <- preset$species_ids
gene_ids <- preset$gene_ids gene_ids <- preset$gene_ids
cached("clusteriness", c(species_ids, gene_ids, use_positions), { cached("clusteriness", c(species_ids, gene_ids), {
results <- data.table(gene = gene_ids) results <- data.table(gene = gene_ids)
# Prefilter the input data by species. # Prefilter the input data by species.
@ -54,12 +54,7 @@ clusteriness <- function(preset, use_positions = FALSE, progress = NULL) {
# Perform the cluster analysis for one gene. # Perform the cluster analysis for one gene.
compute <- function(gene_id) { compute <- function(gene_id) {
data <- if (use_positions) { data <- distances[gene_id, distance]
distances[gene_id, position]
} else {
distances[gene_id, distance]
}
score <- clusteriness_priv(data) score <- clusteriness_priv(data)
if (!is.null(progress)) { if (!is.null(progress)) {

21
R/correlation.R
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@ -1,14 +1,12 @@
# Compute the mean correlation coefficient comparing gene distances with a set # Compute the mean correlation coefficient comparing gene distances with a set
# of reference genes. # of reference genes.
correlation <- function(preset, use_positions = FALSE, progress = NULL) { correlation <- function(preset, progress = NULL) {
species_ids <- preset$species_ids species_ids <- preset$species_ids
gene_ids <- preset$gene_ids gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids reference_gene_ids <- preset$reference_gene_ids
cached( cached(
"correlation", "correlation", c(species_ids, gene_ids, reference_gene_ids), {
c(species_ids, gene_ids, reference_gene_ids, use_positions),
{ # nolint
# Prefilter distances by species. # Prefilter distances by species.
distances <- geposan::distances[species %chin% species_ids] distances <- geposan::distances[species %chin% species_ids]
@ -20,17 +18,10 @@ correlation <- function(preset, use_positions = FALSE, progress = NULL) {
# Make a column containing distance data for each species. # Make a column containing distance data for each species.
for (species_id in species_ids) { for (species_id in species_ids) {
species_data <- if (use_positions) { species_data <- distances[
setnames(distances[ species == species_id,
species == species_id, .(gene, distance)
.(gene, position) ]
], "position", "distance")
} else {
distances[
species == species_id,
.(gene, distance)
]
}
data <- merge(data, species_data, all.x = TRUE) data <- merge(data, species_data, all.x = TRUE)
setnames(data, "distance", species_id) setnames(data, "distance", species_id)

27
R/neural.R
View file

@ -25,10 +25,7 @@ neural <- function(preset, progress = NULL, seed = 49641) {
# Make a columns containing positions and distances for each # Make a columns containing positions and distances for each
# species. # species.
for (species_id in species_ids) { for (species_id in species_ids) {
species_data <- distances[ species_data <- distances[species == species_id, .(gene, distance)]
species == species_id,
.(gene, position, distance)
]
# Only include species with at least 25% known values. As # Only include species with at least 25% known values. As
# positions and distances always coexist, we don't loose any # positions and distances always coexist, we don't loose any
@ -46,26 +43,14 @@ neural <- function(preset, progress = NULL, seed = 49641) {
# However, this will of course lessen the significance of # However, this will of course lessen the significance of
# the results. # the results.
mean_position <- round(species_data[, mean(position)])
mean_distance <- round(species_data[, mean(distance)]) mean_distance <- round(species_data[, mean(distance)])
data[is.na(distance), `:=`(distance = mean_distance)]
data[is.na(distance), `:=`( # Name the new column after the species.
position = mean_position, setnames(data, "distance", species_id)
distance = mean_distance
)]
input_position <- sprintf("%s_position", species_id) # Add the input variable to the buffer.
input_distance <- sprintf("%s_distance", species_id) input_vars <- c(input_vars, species_id)
# Name the new columns after the species.
setnames(
data,
c("position", "distance"),
c(input_position, input_distance)
)
# Add the input variables to the buffer.
input_vars <- c(input_vars, input_position, input_distance)
} }
} }

2
R/preset.R
View file

@ -40,9 +40,7 @@
#' @export #' @export
preset <- function(methods = c( preset <- function(methods = c(
"clusteriness", "clusteriness",
"clusteriness_positions",
"correlation", "correlation",
"correlation_positions",
"neural", "neural",
"proximity" "proximity"
), ),